Combining Classifiers in the Problem of Thematic Processing of Hyperspectral Aerospace Images

Statistical texture features are frequently used for the thematic processing of very high spatial resolution satellite images. The assessment of information content of 1st and 2nd order statistics is carried out based on processing WorldView-2 images of test areas located on the territory of the Savvatyevskoe forestry and employing the corresponding ground-based data. The comparison of the accuracy and computational efficiency of traditional and ensemble classifiers in the problem of pattern recognition of various natural and man-made objects reveals the high performance of the error correcting output codes method. The estimates obtained in this study demonstrate the advantage of using ensemble classification and 2nd order statistical texture features.

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“…Thus the method used is abbreviated as ECOC SVM. The effectiveness of ECOC and in particular ECOC SVM was investigated in [9].…”

Section: Texture Classification Methodsmentioning

confidence: 99%

Information content of statistical texture features in the problem of recognition and mapping of natural and man-made objects from space images

et al. 2020

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“…At the same time, they lose in speed and sustainability of learning outcomes. The ECOC allows us to construct an effective combination of these algorithms [4].…”

Section: The Methods Of Remote Inventory Automationmentioning

confidence: 99%

“…The ECOC uses a coding matrix corresponding to one-vs-one strategy. The rationale for this choice is presented in [4]. For each binary classification problem a basic algorithm is selected to ensure maximum classification rate without significant loss of accuracy.…”

Section: The Methods Of Remote Inventory Automationmentioning

confidence: 99%

Remote Sensing Methods for the Retrieval of Inventory and Bioproductivity Parameters of Forests Using High Resolution Satellite Images

et al. 2019

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A method for automated processing high spatial resolution satellite images is proposed to retrieve inventory and bioproductivity parameters of forest stands. The method includes effective learning classifiers, inverse modeling, and regression modeling of the estimated parameters. Spectral and texture features are used to classify forest species. The results of test experiments for the selected area of Savvatievskoe forestry (Russia, Tver region) are presented. Accuracy estimates obtained using ground-based measurements demonstrate the effectiveness of using the proposed techniques to automate the process of updating information for the State Forest Inventory program of Russia.

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“…where y B ∈ {1, −1} is a label in the binary classification problem, the classification score s(x) means the normalized distance from the sample x to the discriminant surface in the area of the relevant class. The rationale for this choice is described in detail in (Dmitriev et al [5]).…”

Section: Species Classification and Vital State Assessmentmentioning

confidence: 99%

“…The results of ITCD obtained from high spatial resolution aerial images can be used in combination with high spectral resolution images for classifying tree species, growth rate, and regions of missing trees (Maschler et al [3]). A similar approach can be employed to delineate tree growth areas to improve the results of thematic processing medium and high-resolution satellite images (Safonova et al, Dmitriev et al [4,5]).…”

Section: Introductionmentioning

confidence: 99%

Individual Tree Crown Delineation for the Species Classification and Assessment of Vital Status of Forest Stands from UAV Images

Safonova

Hamad

Дмитриев

et al. 2021

Drones

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Monitoring the structure parameters and damage to trees plays an important role in forest management. Remote-sensing data collected by an unmanned aerial vehicle (UAV) provides valuable resources to improve the efficiency of decision making. In this work, we propose an approach to enhance algorithms for species classification and assessment of the vital status of forest stands by using automated individual tree crowns delineation (ITCD). The approach can be potentially used for inventory and identifying the health status of trees in regional-scale forest areas. The proposed ITCD algorithm goes through three stages: preprocessing (contrast enhancement), crown segmentation based on wavelet transformation and morphological operations, and boundaries detection. The performance of the ITCD algorithm was demonstrated for different test plots containing homogeneous and complex structured forest stands. For typical scenes, the crown contouring accuracy is about 95%. The pixel-by-pixel classification is based on the ensemble supervised classification method error correcting output codes with the Gaussian kernel support vector machine chosen as a binary learner. We demonstrated that pixel-by-pixel species classification of multi-spectral images can be performed with a total error of about 1%, which is significantly less than by processing RGB images. The advantage of the proposed approach lies in the combined processing of multispectral and RGB photo images.

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Combining Classifiers in the Problem of Thematic Processing of Hyperspectral Aerospace Images

Cited by 11 publications

References 9 publications

Information content of statistical texture features in the problem of recognition and mapping of natural and man-made objects from space images

Information content of statistical texture features in the problem of recognition and mapping of natural and man-made objects from space images

Remote Sensing Methods for the Retrieval of Inventory and Bioproductivity Parameters of Forests Using High Resolution Satellite Images

Individual Tree Crown Delineation for the Species Classification and Assessment of Vital Status of Forest Stands from UAV Images

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